Revealing post-transcriptional microRNA-mRNA regulations in Alzheimer's disease through ensemble graphs

Exploring the impact of circular RNA on ALS progression: A systematic review

Amyotrophic lateral sclerosis is a neurodegenerative disease that damages motor neurons and causes gradual muscular weakening and paralysis. Although studies have linked a number of genetic and environmental factors to ALS, the specific causes and mechanisms of the disease are still unclear. The pivotal role of circular RNA in the pathogenesis of ALS is a newly emerging area of research. The term "circular RNA" describes a particular class of RNA molecule that, in contrast to most RNA molecules, has a closed-loop structure. According to recent research, circular RNA might be essential for the development and progression of ALS. It has been discovered that these circular RNAs support important cellular functions related to ALS, including protein turnover, mitochondrial function, RNA processing, and cellular transport. Gaining knowledge about the precise roles and processes of circular RNA in the development of ALS could assist in understanding the pathophysiology of the disease and possibly pave the way for the development of targeted therapies. However, the understanding of circular RNA in ALS is still limited, and more research is needed to fully elucidate its role. In order to gain a comprehensive understanding of the role of circRNAs in ALS, it is imperative to delve into the various mechanisms through which circRNAs may contribute to the development and progression of the disease. Examining the current status of circRNA research in ALS and offering insights into their potential as therapeutic targets and diagnostic markers are the primary objectives of this review.

The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.